As shown in Table 6, we compare our proposed method with the other fourteen state-of-the-art methods. In the LBP+RR method, the LBP features were extracted by hand to regress the crowd count. Owing to the scale variation in the crowd, the work of MCNN [1]}, CAFN [2]}, TDF-CNN [3]}, Switch-CNN [4]}, and CP-CNN [5]} employed multi-column CNNs to extract the multi-scale features to cope with the scale variation in the crowd. Instead of using the multi-column CNN, in [6]}, Hossain et al. proposed the scale-aware attention network to encode the multi-scale density maps. In [7]}, IG-CNN adapted the multi-scale crowd scenes by increasing its capacity. The work of Cascaded-MTL [8]}, Marsden et al.[9]}, DecideNet+R3 [10]}, and DDCN [11]} introduced the multi-task learning to assist in reducing the count estimation errors. The work of SE Cycle GAN [12]} designed the data collector and labeler to generate the crowd data with the corresponding labels to alleviate the overfitting caused by limited data for training. In [13]}, the deep negative correlation learning was utilized to extract the general feature representation. The estimation errors are illustrated in Table 6. It can be observed that, firstly, the performance of deep learning is far better than the hand-crafted features. Secondly, lots of efforts are done to extract the multi-scale features of the crowd, which is very important to reduce the estimation errors to some extent. Finally, our proposed SegCrowdNet achieves the best performance in MAE and MSE on both datasets.
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